Applied Machine Learning courses can help you learn data preprocessing, model selection, feature engineering, and evaluation metrics. You can build skills in implementing algorithms, optimizing performance, and interpreting results in practical contexts. Many courses introduce tools like Python, TensorFlow, and scikit-learn, that support developing machine learning models and applying AI techniques to solve real-world problems.
University of Michigan
Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning Methods, Machine Learning, Model Training, Model Optimization, Machine Learning Algorithms, Unsupervised Learning, Python Programming, Classification Algorithms, Artificial Neural Networks
★ 4.6 (8.8K) · Intermediate · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Computer Vision, Model Evaluation, PyTorch (Machine Learning Library), Supervised Learning, Unsupervised Learning, Image Analysis, Applied Machine Learning, Data Preprocessing, Dimensionality Reduction, Machine Learning Methods, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Model Training, Machine Learning, Deep Learning, Model Optimization
★ 3.4 (16) · Intermediate · Specialization · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, Model Training, NumPy, Machine Learning Algorithms, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Model Optimization, Regression Analysis, Algorithms
★ 4.9 (32K) · Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Data Science, Statistical Modeling, Predictive Modeling, Machine Learning Methods, Exploratory Data Analysis, Machine Learning, Data Analysis, Applied Machine Learning, Machine Learning Software, Feature Engineering, Random Forest Algorithm, Supervised Learning, Logistic Regression, Data Processing, Model Optimization, Data Manipulation, Data Visualization
Intermediate · Course · 1 - 4 Weeks

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms
★ 4.9 (39K) · Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Data Collection, Model Optimization, Convolutional Neural Networks, Model Deployment, Transfer Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Machine Learning Software
★ 4.4 (27) · Intermediate · Professional Certificate · 3 - 6 Months

Google Cloud
Skills you'll gain: Model Deployment, Model Optimization, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Model Training, Transfer Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Machine Learning
★ 4.5 (1.5K) · Advanced · Specialization · 3 - 6 Months

Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Matplotlib, Feature Engineering, Time Series Analysis and Forecasting, Data Preprocessing, Jupyter, Image Analysis, Cloud Deployment, Scikit Learn (Machine Learning Library), Applied Machine Learning, Tensorflow, Amazon Web Services, Python Programming, Data Transformation, Logistic Regression, Machine Learning Methods, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML)
★ 4.8 (13) · Beginner · Specialization · 1 - 3 Months

New York University
Skills you'll gain: Supervised Learning, Machine Learning Methods, Model Evaluation, Reinforcement Learning, Applied Machine Learning, Statistical Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Statistical Modeling, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Model Training, Machine Learning, Derivatives, Tensorflow
★ 3.7 (820) · Intermediate · Specialization · 3 - 6 Months

Alberta Machine Intelligence Institute
Skills you'll gain: Data Preprocessing, Data Ethics, Applied Machine Learning, Machine Learning Methods, Machine Learning, Machine Learning Algorithms, Product Lifecycle Management, Case Studies, Data Collection, Data Capture, Supervised Learning, Business Requirements, Data Quality, Business Analysis, Unsupervised Learning, Artificial Intelligence
★ 4.7 (747) · Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Supervised Learning, Model Optimization, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Methods, Statistical Machine Learning, Machine Learning Algorithms, Predictive Modeling, Model Training, Data Preprocessing, Classification Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Data Transformation, Fine-tuning
Advanced · Course · 1 - 3 Months

University of Washington
Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning, Image Analysis, Machine Learning Algorithms, AI Personalization, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Supervised Learning, Bayesian Statistics, Statistical Machine Learning, Model Training, Logistic Regression, Statistical Modeling, Data Mining
★ 4.6 (16K) · Intermediate · Specialization · 3 - 6 Months
Applied machine learning is a branch of artificial intelligence that focuses on using algorithms and statistical models to analyze and interpret complex data. It is important because it enables organizations to make data-driven decisions, automate processes, and enhance user experiences. By leveraging applied machine learning, businesses can uncover insights from vast amounts of data, leading to improved efficiency and innovation across various sectors.‎
Careers in applied machine learning are diverse and growing rapidly. Some potential job titles include Machine Learning Engineer, Data Scientist, AI Research Scientist, and Business Intelligence Analyst. These roles often require a blend of programming skills, statistical knowledge, and domain expertise, allowing professionals to work on projects that range from developing predictive models to creating intelligent systems.‎
To succeed in applied machine learning, you should develop a strong foundation in programming languages such as Python or R, as well as proficiency in data manipulation and analysis. Key skills include understanding algorithms, statistical modeling, data visualization, and machine learning frameworks like TensorFlow or Scikit-learn. Additionally, familiarity with cloud platforms and data engineering concepts can be beneficial.‎
There are many excellent online courses available for learning applied machine learning. Some recommended options include the Applied Machine Learning Specialization and Applied Machine Learning: Techniques and Applications. These courses provide a structured learning path and practical experience to help you build your skills.‎
Yes. You can start learning applied machine learning on Coursera for free in two ways:
If you want to keep learning, earn a certificate in applied machine learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn applied machine learning, start by identifying your current skill level and the specific areas you want to focus on. Enroll in introductory courses to build foundational knowledge, then progress to more advanced topics. Engage in hands-on projects to apply what you learn, and consider joining online communities or forums to connect with others in the field for support and collaboration.‎
Typical topics covered in applied machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, natural language processing, and model evaluation. Courses often emphasize practical applications and real-world case studies to help learners understand how to implement machine learning solutions effectively.‎
For training and upskilling employees in applied machine learning, consider courses like the IBM Machine Learning Professional Certificate or the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These programs are designed to equip professionals with the necessary skills to apply machine learning techniques in their work.‎